Modern Data Warehouse For Organizations
Have you ever thought of an organization completely gaining insights from a data residing in a database, further leading to be dependent on a data warehouse? Yes, no matter how dependent an organization is on a data warehouse, it should be modernized. Most of the customers are looking to ingest the data from multiple sources, so that they can make intelligent decisions based on that data. This article explains the importance of modernizing a data warehouse from an organization’s perspective.
Present Scenario with Traditional Warehouse
Anything that is not normalized will lead to a much larger data. This leads to a data redundancy which further leads to large storage area. So, when data increases, a question like how fast we can extract the data arises. We can achieve such a performance by building a data warehouse. The purpose of this is to provide aggregated and calculated data like averages, totals, trends and so on in a suitable format for decision making. The decision making is not dependent on a simple data that shows which customer paid this bill or who brought this product in a day. It would be on general trends, so that the efforts of the organization would best be used to help them improve or modernize the traditional data warehouse.
The promise of the warehouse is to receive the advances of different external data sources like social media, real-time sources and semi structured data. A doubt might arise on what would be the benefit with such kind of data from sources like social media.
For example, consider we have a product, we would like to know the feedback on it, once it is out in the market. So, by using a platform like twitter, we would like to know how the product is performing. In order to know this feedback in an easy way, we provide a platform where we integrate this into our organization’s data warehouse. Once we start addressing the drawbacks received from twitter, then there might be improvement on the product sales. In this way, we can say that social media data can also be used for important business decisions.
Traditional Data Warehouse to Modern Data Warehouse
A traditional data warehouse fails in this aspect to keep up with all the changes. In order to overcome this, we need to use a solution that makes the best use of emerging technologies around the world, such as automation capabilities, visualization tools and cloud platforms. Defining this in a simpler way is to have a unified platform with the ability to carry out high performance analytics on structured as well as unstructured data.
Traditional data warehouses were designed to consume flat file structures or data from other relational systems, but today it’s no longer the case. The data can be extracted in a json or xml format or binary video format which could be completely unstructured data with which a data scientist cannot perform the required processing so easily. In order to overcome these challenges, we need to shift to Modern Data Warehouse. This will overcome all the storage limitations of loading large data before it can be analyzed.
As an organization, we must look at the environment and data storage from an Infrastructure as a Service (IaaS) perspective and Platform as a Service (PaaS) perspective. As data is growing and with the company’s growth rate, you might think of cloud and start deciding on whether to go with IaaS or PaaS.
The main difference between IaaS and PaaS is that from an IaaS perspective, you might say that you don’t require a hardware anymore and that you will get a virtual machine with all the software into the cloud and will be just managing it from a security perspective. We can say that we are no longer dealing with hardware and infrastructure. We are totally implementing that into the cloud. But, from a PaaS perspective, we are eliminating the management of both software and hardware, thus allowing the cloud provider to help manage our security. A lot of the security breaches have come up from an IaaS perspective.
How Useful Can a Modern Data Warehouse be?
Modern data warehouse is nothing but a customer solution pattern on modernizing the infrastructure. When we say infrastructure, it may be on-premises or new one in terms of business scenarios. Majority of the time goes in cleaning of data that comes from a variety of sources. Doing data quality is critical as a part of modern data warehouse architecture.
The data coming from systems like CRM would be in a structured way, but what about the data that comes in an unstructured format like IoT data or social media data to perform analytics on? Analytics should be meaningful like building a modern data warehouse which would be handy.
The customers ingest this data from a variety of sources and store it in a data lake architecture. The data storage will then be accessed either by a data scientist or data analyst or even data engineer who then starts to prepare and clean the data. As mentioned earlier, the data quality would be an important part of modern data warehouse architecture.
Once the data is cleaned, then modelling that data or serving that data needs to be done. The business might have many questions. So, creating a dimensional model or a structured model of data would be helpful for the business to be understood.
Then comes the advanced analytics that are built on top of this solution. This pattern has become emblematic for a lot of customers who are moving their data warehouse solutions to the cloud. This is where they achieve the modernizing of the data warehouse to cloud.
Azure Data Factory
Let’s dive into one of the cloud services in Azure – Azure Data factory. It is a serverless integration product which enables data integration in a hybrid scenario. The source might be from anywhere and in any form, but we will be storing that data into the azure Data Lake storage. Once the data is landed here, then comes the critical phase of data preparation and data cleaning as discussed earlier. There are numerous products to explore the data like Azure SQL Data warehouse with SQL Polybase capabilities that allows you to query the data that present in the data lake or data warehouse. Other products like data bricks allow you to use other languages like Python, Scala for querying and exploring the data in the data lakes. This is where the customers with different skill sets would be having an advantage. There are a lot of choices on the skill set.
This architecture brings that data to a data store and promotes open data architecture which says we have extracted that data and can perform some data preparations and data explorations. Then, we would be ready to publish that data to the organization in a place where we typically use a data warehouse. Publishing this data to the business users is important and that’s where SQL data warehouse comes in handy by separating the computing storage which inherently allows to scale up the computation process with more users to work around and scale it down when we have less traffic. This is how customers bring it together and then unlock the data of their organizations.
As the data is increasing day by day, there is a need for a platform which should provide a solution that would be easy for organizations to access the data. One such solution would be modernizing the data warehouse, so that it would be directed into a helpful decision making for their business customers and enterprises.
However, not only modernizing the data warehouse, but also providing high security to this data is equally important. Protecting a modern cloud data warehouse with something like multifactor authentication, single sign-on and IP whitelisting is important in terms of federated identification.
Contact for further details
Sowri Vivaswath C
Team Lead – Analytics Information Management